The subject matter disclosed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Template matching is a technique to recognize content in an image. The template matching techniques include a feature point based template matching that extracts features from an input image and a model image. The features are matched between the model image and the input image with K-nearest neighbor search. Thereafter, a homography transformation is estimated from the matched features, which may be further refined. However, the feature point based template matching technique works well only when images contain a sufficient number of interesting feature points. Further, the feature point based template matching fails to produce a valid homography, and thus result in ambiguous matches.
Further, the template matching techniques include a technique to search an input image by sliding a window of a model image in a pixel-by-pixel manner, and then computing a degree of similarity between the input image and the model image, where the similarity is given by correlation or normalized cross correlation. However, pixel-by-pixel template matching is very time-consuming and computationally expensive. Further, the searching for the input image with arbitrary orientation in the model image makes the template matching technique far more computationally expensive.
Therefore, there may be a need for an improved system and a method for template matching in an image or a video that may be cost effective, robust, efficient, and may reduce computation time.